Computer data simulator to assess the accuracy of estimates of visual N2/N2pc event-related potential components

Marturano, Francesca; Brigadoi, Sabrina; Doro, Mattia; Roberto Dell’Acqua; Sparacino, Giovanni · 2020 · Crossref

DOI: 10.1088/1741-2552/ab85d4

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Summary

This paper addresses the challenge of accurately estimating visual N2 and N2pc event-related potential (ERP) components, which are critical markers for visual attention allocation but suffer from low amplitudes relative to background electroencephalogram (EEG) noise. A primary motivation for this work is the arbitrary nature of selecting the number of averaged sweeps ($N_{swp}$) in visual search studies, where previous research has utilized widely varying ranges (50–500 sweeps) without rigorous justification. To resolve this, the authors developed a computer data simulator capable of assessing estimation accuracy as a function of signal-to-noise ratio (SNR) and $N_{swp}$, providing quantitative guidelines for experimental design. The methodology involved creating a realistic synthetic dataset based on real experimental data collected from 14 participants performing a visual search task. The authors first derived ERP templates using Gaussian mixture models fitted to grand-averaged ERPs from real data, capturing the variability of the N2 component. They then generated noise-free synthetic sweeps by perturbing these templates to emulate intra-individual variability. To simulate realistic noise, resting-state EEG recordings from two additional participants were processed and scaled to create noise matrices with controlled power levels. These noise signals were added to the synthetic ERPs to generate three datasets with distinct SNR ranges ([0–0.4], [0.4–0.8], and [0.8–1.2]). The accuracy of conventional averaging (CA) was evaluated by varying $N_{swp}$ from 10 to 100 and measuring metrics such as absolute error in peak amplitude and latency, and deviation from the true mean around the peak. The results demonstrated that both $N_{swp}$ and SNR significantly impact the accuracy of N2/N2pc estimates. Crucially, the simulation revealed that for any given SNR level, there exists a specific, non-arbitrary threshold for $N_{swp}$ beyond which no significant improvements in noise suppression or estimation accuracy occur. This finding allows researchers to determine the optimal number of sweeps required for reliable detection rather than relying on arbitrary conventions. The simulator successfully replicated the variability observed in real data, validating its utility for testing estimation methods. The significance of this work lies in providing investigators with a flexible, quantitative tool for designing experimental protocols. By enabling the parametric determination of $N_{swp}$ based on expected SNR, the simulator helps optimize experimental duration and data reliability. Furthermore, the framework is adaptable, allowing parameters to be tuned or extended to fit other ERP modulations, thereby offering a more rigorous approach to validating ERP estimation techniques compared to previous ad hoc simulations or complex physiological models.

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• signal detection theory